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Medical protocols and guidelines can be looked upon as concurrent programs, where the patient's state dynamically changes over time. Methods based on verification and model-checking developed in the past have been shown to offer insight into the correctness of guidelines and protocols by adopting a logical point of view. However, there is uncertainty involved both in the management of the disease and the way the disease will develop, and, therefore, a probabilistic view on medical protocols seems more appropriate. Representations using Bayesian networks capture that uncertainty, but usually concern a single patient group and do not capture the dynamic nature of care. In this paper, we propose a new method inspired by automata learning to represent and identify patient groups for obtaining insight into the care that patients have received. We evaluate this approach using data obtained from general practitioners and identify significant differences in patients who were diagnosed with a transient ischemic attack. Finally, we discuss the implications of such a computational method for the analysis of medical protocols and guidelines.

Medical image interpretation is a difficult problem for which human interpreters, radiologists in this case, are normally better equipped than computers. However, there are many clinical situations where radiologist's performance is suboptimal, yielding a need for exploitation of computer-based interpretation for assistance. A typical example of such a problem is the interpretation of mammograms for breast-cancer detection. For this paper, we investigated the use of Bayesian networks as a knowledge-representation formalism, where the structure was drafted by hand and the probabilistic parameters learnt from image data. Although this method allowed for explicitly taking into account expert knowledge from radiologists, the performance was suboptimal. We subsequently carried out extensive experiments with Bayesian-network structure learning, for critiquing the Bayesian network. Through these experiments we have gained much insight into the problem of knowledge representation and concluded that structure learning results can be conceptually clear and of help in designing a Bayesian network for medical image interpretation.

Cancer-patient management in the context of a multi-center treatment trial requires following a complex detailed process involving multispecialty patient treatment as well as study-related tasks, described in free-text protocol documents. We present a process-oriented approach for modelling clinical trial treatment protocols (CTTPs) to be used for enabling applications that support protocol-based care process delivery, monitoring and analysis. This modelling approach provides an intuitive visual representation of the protocol document catering for change management, intra-center and national adaptations to the master protocol, and multi-level share-ability. The methodology can be re-used in CTTPs of different cancer domains due to the similarity of the CTTPs in terms of required content.

Medical research would benefit from automatic methods that support eligibility evaluation for patient enrollment in clinical trials and design of eligibility criteria. In this study we addressed the problem of formalizing eligibility criteria. By analyzing a large set of breast cancer clinical trials we derived a set of patterns, that capture typical structure of conditions, pertaining to syntax and semantics. We qualitatively analyzed their expressivity and evaluated coverage using regular expressions, running experiments on a few thousands of clinical trials also related to other diseases. Based on an early evaluation we conclude that derived patterns cover the language of eligibility criteria to a large extent and may serve as a semi-formal representation. We expect that extending the presented method for pattern recognition with recognition of ontology concepts will facilitate generating computable queries and automated reasoning for various applications.